Image Classification
Keras
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  license: apache-2.0
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- 1. Project Title and Description
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- 2. Purpose
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- 3. Installation Instructions
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- 4. Usage Instructions
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- 5. Model Architecture
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- 6. Training Details
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- 7. Evaluation
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- 8. Examples
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- 9. Contributing
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- 10. License
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- # CNN Image Classifier
 
 
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- ## Description
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- This project provides a Convolutional Neural Network (CNN) model for classifying images as either 'real' or 'fake'. The model is based on the ResNet50 architecture and has been fine-tuned for binary classification tasks.
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- ## Purpose
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- The CNN model is designed to classify images into two categories: 'real' and 'fake'. This can be useful for various applications, including detecting AI-generated content.
 
 
 
 
 
 
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- ## Installation
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- Ensure you have the following dependencies installed:
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- ```bash
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- pip install tensorflow numpy opencv-python scikit-learn
 
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  license: apache-2.0
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+ Real vs AI-Generated Image Classification
 
 
 
 
 
 
 
 
 
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+ This project provides a Convolutional Neural Network (CNN) model for classifying images as either 'real' or 'fake'.
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+ CNN is a type of deep learning model specifically designed to process and analyze visual data by applying convolutional layers that automatically detect patterns and features in images.
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+ Our CNN model is based on 2,800 real images and AI-generated images, which are divided equally.
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+ Our goal is to accurately classify the source of the image with at least 85% accuracy and achieve at least 80% in the Recall test.
 
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+ 5. Installation Instructions
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+ 6. Usage Instructions
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+ 7. Model Architecture
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+ 8. Training Details
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+ 9. Evaluation
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+ 10. Examples
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+ 11. Contributing
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+ 12. License
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